CRAIG RISI – Head of Engineering, Old mutual
HOW AI IS EVOLVING SOFTWARE QUALITY AND TESTING
In the realm of software testing, the integration of Artificial Intelligence (AI) is revolutionizing traditional approaches and shaping the future of quality assurance. This transformation comes as a response to the challenges posed by the increasing complexity of software applications and the demand for faster release cycles without compromising quality.
AI is playing a pivotal role in test automation, alleviating the time-consuming nature of manual testing. It facilitates the creation and maintenance of test scripts, with the added advantage of intelligent test cases and data generation. By leveraging AI algorithms, testing processes can automatically adapt to application behavior and usage patterns, resulting in more comprehensive test coverage and efficient identification of defects.
The benefits of incorporating AI into software testing are numerous, including improved test efficiency, enhanced coverage, early defect detection, and optimized resource utilization. However, challenges such as a skills gap, initial implementation costs, and ethical considerations need to be navigated. Real-world applications and case studies showcase how companies have successfully implemented AI, yielding measurable improvements in testing outcomes.
Looking forward, the continuous evolution of AI in testing presents exciting possibilities. Emerging trends include the integration of AI with other methodologies like DevOps and the exploration of autonomous testing, where AI systems make decisions and execute tests without human intervention. As we navigate this evolving landscape, organizations are encouraged to explore and embrace AI in their testing processes, staying informed about the latest advancements in AI and testing technologies for a more efficient and effective software testing future.
ZOLANI RATYA – Test Manager, Santam
WHY MANUAL TESTERS FEEL INTIMIDATED BY AUTOMATION TESTING?
Manual testers often experience apprehension and reluctance towards automation testing due to various reasons. This hesitancy may stem from factors such as unfamiliarity with automation tools, fear of job displacement, or a perceived learning curve associated with transitioning from manual to automated testing methodologies. Understanding and addressing these concerns is crucial for successful integration of automation testing in a testing environment.
DESMOND MATHEBULA – CEO, Ntokoto Technology Group
THE FUTURE OF TESTING DATA SERVICES PLATFORMS THROUGH DATAOPS
It is becoming more popular now that Data Quality Engineering to be embedded into DataOps with a dedicated testing team unlike when I started Evangelizing for it in year 2011. Now that adoption is no longer a challenge, how does the future looks for Data Quality Engineering community?
KEITUMETSE SEKETE – Senior Test Analyst, BMW IT Hub
DISRUPTING INERTIA: ELEVATING QUALITY ASSURANCE THROUGH DIGITAL TRANSFORMATION WITH APACHE JMETER, SOAP UI, POSTMAN, AND INSOMNIA
Disrupting inertia in the realm of digital transformation for Quality Assurance has ushered in a positive wave, impacting accuracy, customer satisfaction, efficiency, and overall performance. Gone are the days of manual performance testing with synchronized clicks in a lab setting. Now, tools like Apache JMeter offer streamlined configurations, providing detailed reports on response times for various user scenarios and loads.
The evolution of digital transformation has not only made testing tools more intuitive but has also introduced diverse functionalities and reporting capabilities.
API testing has surged in popularity, with tools such as SOAP UI, Postman, and the recent addition, Insomnia.
As Quality Assurance undergoes a transformation, tools are becoming more intelligent, and automation is becoming increasingly prevalent. These disruptions may be on the rise, but they contribute to enhanced software quality and elevate the role of Test Analysts.
SUMIT PAL – Independent Consultant
AI-BASED TESTING AND VERIFICATION
This session will discuss automation, test data management, test scripting, exploratory testing enhanced testing with the Large Language Model (LLM)
- Identify opportunities to improve test quality with AI
- Construct test automation with AI tools
- Ideas during exploratory testing using AI tools
- Leverage AI tools to aid the design process of new features
- Improve testability with AI tools
- Maximize output with prompt engineering
FIKILE MEKGOE – Head of Technology: Delivery & Testing, Bidvest Bank IT
MOBILE APPLICATION AUTOMATED TESTING
The modern business landscape demands swift market delivery for competitiveness. Manual testing methods often hinder timely mobile application project completion. Mobile application automated testing involves using software tools and scripts to automatically execute test cases, ensuring functionality, performance, and usability across diverse devices and platforms. This approach is vital for guaranteeing app quality and reliability. Overall, mobile application automated testing enhances development by boosting efficiency, expanding test coverage, cutting costs, and ensuring top-notch apps reach users, providing excellent customer experiences within budget constraints.
MFUNDO DELIHLAZO – Practice Lead: Quality Engineering, Sasol
PERFORMANCE TESTING IN AN DEVOPS WORLD: TIME TO SHIFT LEFT
With the industries adopting short delivery cycles (Agile) for a quicker time to market and to gain competitive advantage. It becomes important to ensure that the performance of system/product components is of acceptable standard.
Traditionally, the performance of any application is assessed towards the end of the life cycle, just before production. I believe that a mindset shift is necessary to ensure that we build scalable applications that meet the ever-growing customer demands. Hence, it is more important now than ever before that performance testing is embedded earlier in the project lifecycle not at the end as the norm.
Integrating performance testing in the DevOps pipelines will ensure that every application is tested thoroughly for both performance and functionality.
He will be focusing on three key points in this Talk
- Continuous performance testing
- Challenges encountered in embedding performance testing early
- Best practices to ensure performance testing is done well in DevOps
ERNST GROSSE-HEITMEYER – Sr. Automation Specialist, Momentum Metropolitan
TEST AUTOMATION TO BETTER WORK IN AGILE MODE- A CASE STUDY
This case study explains the integration of test automation to optimize the Agile software development process within projects. It includes some steps taken from tool selection to tool implementation and environment management, identifying the before and after benefits of automation implementation in an Agile environment.
